The RMSECV value was computed over the whole spectral range (1000-2499 nm) by the MCCV method, setting the number of MC simulations to 500 and the ratio of training samples (relative to the total number of samples)to 0.75.
All RMSECV values were computed by the MCCV method using the parameters presented in the flow chart (Figure 1).
The efficiencies of various preprocessing methods were assessed by the RMSECV value computed by MCCV. Among these methods, D-WT achieved the optimal pretreatment.
Given the MD burden in Brazil (5) and to better understand the impact of the vaccination strategy implemented, we conducted a study to examine MD occurrence in the Federal District from 2005 to 2011 and to assess the direct impact of the introduction of MCCV on the targeted age group for vaccination.
The MCCV was introduced in the Federal District in August 2010.
To assess the potential impact of MCCV, we compared incidence rates before (2009) and after (2011) vaccine introduction.
The study results showed that MD was a major public health problem in the Federal District due to its morbidity and mortality especially before the introduction of MCCV. The extent of MD in a community has been strongly associated with local living conditions, (14) so these results were unexpected considering that the Federal District has one of the highest HDIs in Brazil.
The number of PLS components was estimated using F-test of MCCV. In this work, random splitting of the training set was performed for 100 times and each time 70% of the training objects were used for developing a PLS model and 30% for prediction.
The root mean squared error of MCCV (RMSEMCCV) value was slightly reduced by smoothing (2.274), taking D2 (1.921), and SNV transformation (1.981) compared with PLS with raw data (2.388).
, but to my mind the latter is rat her different and must be based instead on another independent draft.
was then used to estimate the number of latent variables (from 1 to 7); the training class was randomly split into secondary training class (50%) and secondary predicting class (50%) for 100 times; by calculating and simulating, the minimal MRMCCV value was obtained with a four-component model.
was used to estimate the number of PLSDA latent variables; the training set was randomly divided into a secondary training set (50%) and a secondary predicting set (50%) for 20 times.